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DEF-Net: A Dual-Encoder Fusion Network for Fundus Retinal Vessel Segmentation

Li, Jianyong; Gao, Ge; Yang, Lei; Liu, Yanhong; Yu, Hongnian


Jianyong Li

Ge Gao

Lei Yang

Yanhong Liu


The deterioration of numerous eye diseases is highly related to the fundus retinal structures, so the automatic retinal vessel segmentation serves as an essential stage for efficient detection of eye-related lesions in clinical practice. Segmentation methods based on encode-decode structures exhibit great potential in retinal vessel segmentation tasks, but have limited feature representation ability. In addition, they don’t effectively consider the information at multiple scales when performing feature fusion, resulting in low fusion efficiency. In this paper, a newly model, named DEF-Net, is designed to segment retinal vessels automatically, which consists of a dual-encoder unit and a decoder unit. Fused with recurrent network and convolution network, a dual-encoder unit is proposed, which builds a convolutional network branch to extract detailed features and a recurrent network branch to accumulate contextual features, and it could obtain richer features compared to the single convolution network structure. Furthermore, to exploit the useful information at multiple scales, a multi-scale fusion block used for facilitating feature fusion efficiency is designed. Extensive experiments have been undertaken to demonstrate the segmentation performance of our proposed DEF-Net.


Li, J., Gao, G., Yang, L., Liu, Y., & Yu, H. (2022). DEF-Net: A Dual-Encoder Fusion Network for Fundus Retinal Vessel Segmentation. Electronics, 11(22), Article 3810.

Journal Article Type Article
Acceptance Date Nov 18, 2022
Online Publication Date Nov 19, 2022
Publication Date 2022
Deposit Date Nov 29, 2022
Publicly Available Date Nov 29, 2022
Journal Electronics
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 11
Issue 22
Article Number 3810
Keywords retinal vessel segmentation, encode-decode structure, multiscale fusion
Public URL


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